SOTAVerified

Graph Representation Learning

The goal of Graph Representation Learning is to construct a set of features (‘embeddings’) representing the structure of the graph and the data thereon. We can distinguish among Node-wise embeddings, representing each node of the graph, Edge-wise embeddings, representing each edge in the graph, and Graph-wise embeddings representing the graph as a whole.

Source: SIGN: Scalable Inception Graph Neural Networks

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–100 of 982 papers

TitleStatusHype
Leveraging Joint Predictive Embedding and Bayesian Inference in Graph Self Supervised LearningCode0
Spectro-Riemannian Graph Neural Networks—0
Contrastive Learning Meets Pseudo-label-assisted Mixup Augmentation: A Comprehensive Graph Representation Framework from Local to GlobalCode0
Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space—0
Deep Modularity Networks with Diversity--Preserving Regularization—0
Graph Representation Learning with Diffusion Generative Models—0
Optimizing Blockchain Analysis: Tackling Temporality and Scalability with an Incremental Approach with Metropolis-Hastings Random Walks—0
Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic GraphsCode0
Enhancing Graph Representation Learning with Localized Topological FeaturesCode1
Benchmarking Graph Representations and Graph Neural Networks for Multivariate Time Series ClassificationCode0
CureGraph: Contrastive Multi-Modal Graph Representation Learning for Urban Living Circle Health Profiling and PredictionCode0
Optimizing Supply Chain Networks with the Power of Graph Neural Networks—0
KAN KAN Buff Signed Graph Neural Networks?—0
NoiseHGNN: Synthesized Similarity Graph-Based Neural Network For Noised Heterogeneous Graph Representation LearningCode0
Data-Driven Self-Supervised Graph Representation LearningCode0
LASE: Learned Adjacency Spectral EmbeddingsCode0
Line Graph Vietoris-Rips Persistence Diagram for Topological Graph Representation LearningCode0
A Deep Probabilistic Framework for Continuous Time Dynamic Graph GenerationCode0
GNN-Transformer Cooperative Architecture for Trustworthy Graph Contrastive LearningCode0
Scam Detection for Ethereum Smart Contracts: Leveraging Graph Representation Learning for Secure Blockchain—0
A Comparative Study on Dynamic Graph Embedding based on Mamba and Transformers—0
Multi-Class and Multi-Task Strategies for Neural Directed Link PredictionCode0
RingFormer: A Ring-Enhanced Graph Transformer for Organic Solar Cell Property PredictionCode0
Bootstrapping Heterogeneous Graph Representation Learning via Large Language Models: A Generalized Approach—0
Why Does Dropping Edges Usually Outperform Adding Edges in Graph Contrastive Learning?Code0
Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node ClassificationCode0
Repository-Level Graph Representation Learning for Enhanced Security Patch DetectionCode1
Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning—0
A Self-guided Multimodal Approach to Enhancing Graph Representation Learning for Alzheimer's Diseases—0
Expressivity of Representation Learning on Continuous-Time Dynamic Graphs: An Information-Flow Centric Review—0
GQWformer: A Quantum-based Transformer for Graph Representation Learning—0
From ChebNet to ChebGibbsNetCode0
Toward Fair Graph Neural Networks Via Dual-Teacher Knowledge Distillation—0
Perturbation Ontology based Graph Attention Networks—0
GrokFormer: Graph Fourier Kolmogorov-Arnold TransformersCode1
Instance-Aware Graph Prompt Learning—0
TANGNN: a Concise, Scalable and Effective Graph Neural Networks with Top-m Attention Mechanism for Graph Representation LearningCode0
Conditional Distribution Learning on GraphsCode0
A survey on Graph Deep Representation Learning for Facial Expression Recognition—0
Shedding Light on Problems with Hyperbolic Graph Learning—0
An Efficient Memory Module for Graph Few-Shot Class-Incremental LearningCode0
Variational Graph Contrastive LearningCode0
HeteroSample: Meta-path Guided Sampling for Heterogeneous Graph Representation Learning—0
Learning From Graph-Structured Data: Addressing Design Issues and Exploring Practical Applications in Graph Representation Learning—0
Post-Hoc Robustness Enhancement in Graph Neural Networks with Conditional Random Fields—0
Centrality Graph Shift Operators for Graph Neural NetworksCode0
Non-Euclidean Mixture Model for Social Network EmbeddingCode0
Query-Efficient Adversarial Attack Against Vertical Federated Graph LearningCode0
Exploring Consistency in Graph Representations:from Graph Kernels to Graph Neural NetworksCode0
DECRL: A Deep Evolutionary Clustering Jointed Temporal Knowledge Graph Representation Learning Approach—0
Show:102550
← PrevPage 2 of 20Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Pi-net-linearError (mm)0.47—Unverified